Abstract: The COVID-19 epidemic is still rampant around the world. Wearing masks can effectively block the spread of novel coronavirus, while mask wearing detection can timely remind people in public places to wear masks. To solve the problem and the difficulty of small scale target detection, an improved network model Face_mask Net based on the YOLOv3 algorithm is proposed for mask wearing detection. Because the network model trained by the YOLOv3 algorithm has a low detection rate of small targets,the same IoU value cannot reflect whether the prediction frame and the target frame intersect, and the traditional NMS often produces false suppression for occlusion, the algorithm in this paper improves the residual block and neural network structure, introduces SPP module and CSPNet network module, and adopt DIoU as the loss function and DIoU-NMS as the classifier. The experimental results show that Face_mask Net can effectively improve the target detection accuracy, and the average accuracy of AP75 is 58.05%, which is 4.11 percentage points higher than that of the network model trained by the Yolov3 algorithm.

Key words: YOLOv3, DIoU, spatial pyramid pooling(SPP), mask wearing detection, CSPNet